Using Census Data at TRPA

Introduction

The U.S. Census Bureau collects and distributes data under a handful of different programs. Two of the more commonly used programs are the Decennial Census and the American Community Survey (ACS). The Decennial Census is a definite source of demographic data but only is collected every ten years; it includes a limited number of variables such as number of households and total population. The ACS is a program that provides data estimates on a one, three, and five year timeline; ACS data is collected more frequently but the data estimates have a margin of error that must considered because the data is taken from a small sample of the total population. The ACS includes many more variables compared to the Decennial Census that relate to transportation, income, and housing. Both the Decennial and ACS datasets have similiar data structures. Each row in both datasets include a particular variable and a number the indicates the total number of households or persons that characterize that variable.

Summary of TRPA Census Data

Population and Housing

  nv_acs <- get_acs(geography = "tract", year=2016, 
                variables =  "B01003_001", 
                state = "NV", county=c("Washoe", "Douglas")) %>%
  mutate(data_source="2016 ACS 2016 5-year Estimate")
ca_acs <- get_acs(geography = "tract", year=2016, 
                variables =  "B01003_001", 
                state = "CA",county=c("El Dorado", "Placer")) %>%
  mutate(data_source="2016 ACS 2016 5-year Estimate")
ca_decen <- get_decennial(geography="tract", variables= c("H001001", "P001001"), 
                          state= "CA", year= 2010, county=c("El Dorado", "Placer")) %>% 
  rename(estimate=value) %>% mutate(moe=0, data_source="2010 Decennial Census")
nv_decen <- get_decennial(geography="tract", variables= c("H001001", "P001001"), 
                          state= "NV", year= 2010, county=c("Washoe", "Douglas")) %>% 
  rename(estimate=value) %>% mutate(moe=0, data_source="2010 Decennial Census")
all<- bind_rows(nv_decen,ca_decen, ca_acs, nv_acs) %>%
  left_join(data.frame(tract), by="GEOID") %>%
    filter(!is.na(STATEFP)) %>%
  group_by(variable, data_source) %>% summarise(total=sum(estimate), moe=sum(moe)) %>%
  mutate(variable_name = case_when (variable== "H001001" ~ "Housing Units",
                                    variable== "P001001" ~ "Total Population",
                                    variable== "B01003_001" ~ "Total Population"),
         total=format(total, big.mark=",", scientific=FALSE),
         moe=format(moe, big.mark=",", scientific=FALSE)) %>%
  select(variable_name, variable, total,moe, data_source)
datatable(all, extensions = 'Buttons',
rownames=F,options=list(dom='t', 
          columnDefs = list(list(className = 'dt-center', targets = 0:1))), 
  class = 'cell-border stripe', 
colnames = c('Variable Name', 'Code', 'Total', 'Margin of Error','Data Source'))

Travel Mode to Work

work_transport <- c(Drive= "B08301_002",
                    Walk= "B08301_019",
                    Bike = "B08301_018",
                    `Public Transport` = "B08301_010",
                    `Work from Home` = "B08301_021",
                    Other = "B08301_020",
                    Motorcycle = "B08301_017", 
                    Taxi = "B08301_016")
  nv <- get_acs(geography = "tract", year=2016, 
                variables =  work_transport, 
                state = "NV", geometry = TRUE, summary_var = "B08301_001" )
  ca <- get_acs(geography = "tract", year=2016, 
                variables = work_transport, summary_var = "B08301_001", 
                state = "CA", geometry= TRUE)
   all<- rbind(nv, ca) %>%
    left_join(data.frame(tract), by="GEOID") %>%
    filter(!is.na(STATEFP)) %>%
    dplyr::select(GEOID, NAME.x, variable, estimate, moe, summary_est, summary_moe, County) %>%
    data.frame() %>% select(-geometry.x) %>%
    mutate(source="2016 ACS 2016 5-year Estimate") %>%
    group_by(variable,source ) %>% 
    summarise(number= sum(estimate), total=sum(summary_est), moe=sum(moe)) %>%         
    mutate(total=format(total, big.mark=",", scientific=FALSE),
           number=format(number, big.mark=",", scientific=FALSE),
         moe=format(moe, big.mark=",", scientific=FALSE)) %>%
  select(variable, number, moe,total, source)
  datatable(all, extensions = 'Buttons',
rownames=F,options=list(dom='t', 
          columnDefs = list(list(className = 'dt-center', targets = 0:1))), 
  class = 'cell-border stripe', 
colnames= c ('Variable Name', 'Estimate', 'Margin of Error', 'Total Households','Data Source'))

Household Size

hhsize<- c(`Household Size - 1 Person`="H013002",
           `Household Size - 2 Person`= "H013003",
           `Household Size - 3 Person`="H013004",
            `Household Size - 4 Person`= "H013005",
            `Household Size - 5 Person`= "H013006",
           `Household Size - 6 Person` = "H013007", 
           `Household Size - 7 Person or More` = "H013008")
  nv <- get_decennial(geography = "tract", year=2010, 
                variables =  hhsize, county=c("Washoe", "Douglas"),
                state = "NV", geometry = F, summary_var = "H013001" )
  ca <- get_decennial(geography = "tract", year=2010, county=c("El Dorado", "Placer"),
                variables = hhsize, summary_var = "H013001", 
                state = "CA", geometry= F)
  all<- bind_rows(nv, ca) %>%
    left_join(data.frame(tract), by="GEOID") %>%
    filter(!is.na(STATEFP)) %>%
    dplyr::select(GEOID, NAME.x, variable, value, County, summary_value) %>%
    data.frame() %>%
    mutate(data_source="2010 Decennial Census") %>%
    group_by(variable, data_source) %>% summarise(number=sum(value), total=sum(summary_value)) %>%
   mutate( total=format(total, big.mark=",", scientific=FALSE),
           number=format(number, big.mark=",", scientific=FALSE)) %>%
  select(variable, number, total, data_source)
  
  datatable(all, extensions = 'Buttons',
rownames=F,options=list(dom='t', 
          columnDefs = list(list(className = 'dt-center', targets = 0:1))), 
  class = 'cell-border stripe', colnames = c('Variable Name', 'Count', 'Total','Data Source'))

Demographics: Race

race<- c(`White alone`="P003002",
           `Black or African American alone`= "P003003",
           `American Indian and Alaska Native alone`="P003004",
            `Asian alone`= "P003005",
            `Native Hawaiian and Other Pacific Islander alone`= "P003006",
           `Some Other Race alone` = "P003007", 
           `Two or More Races` = "P003008")
  nv <- get_decennial(geography = "tract", year=2010, 
                variables =  race, county=c("Washoe", "Douglas"),
                state = "NV", geometry = F, summary_var="P003001" )
  ca <- get_decennial(geography = "tract", year=2010, county=c("El Dorado", "Placer"),
                variables = race, summary_var="P003001", 
                state = "CA", geometry= F)
  
all<- bind_rows(nv, ca) %>%
    left_join(data.frame(tract), by="GEOID") %>%
    filter(!is.na(STATEFP)) %>%
    group_by(variable) %>% summarise(value=sum(value), total=sum(summary_value)) %>%
    mutate(source="2010 Decennial Census")

datatable(all, extensions = 'Buttons',
rownames=F,options=list(dom='t', 
          columnDefs = list(list(className = 'dt-center', targets = 0:1))), 
  class = 'cell-border stripe', colnames = c('Variable Name', 'Count', 'Total','Data Source'))

Browse all ACS Variables

Search through the list below to determine which variable(s) you want to analyze. You can download all of the variables

acs_var <- load_variables(2016, "acs5", cache = TRUE)

datatable(acs_var, extensions = 'Buttons',
rownames=F,options=list(pageLength = 15, dom = 'Bfrtip',buttons = c('csv','pdf'), 
          columnDefs = list(list(className = 'dt-center', targets = 0:1))), 
  class = 'cell-border stripe')

Browse all Decennial Variablesss

Search through the list below to determine which variable(s) you want to analyze.

decen_var <- load_variables(2010, "sf1", cache = TRUE)

datatable(decen_var, extensions = 'Buttons',
rownames=F,options=list(pageLength = 15, dom = 'Bfrtip',buttons = c('csv','pdf'), 
          columnDefs = list(list(className = 'dt-center', targets = 0:1))), 
  class = 'cell-border stripe')

TRPA Census Tract

tmap_mode("view")
tm_shape(tract)+ tm_polygons()

Download the Data Tahoe Open Data

datatable(tract %>% data.frame() %>% select(GEOID, County) %>% mutate(Note="TRPA Census Tracts"), 
          extensions = 'Buttons',
rownames=F,options=list(pageLength = 10, dom = 'Bfrtip',buttons = c('csv','pdf'), 
          columnDefs = list(list(className = 'dt-center', targets = 0:1))), 
  class = 'cell-border stripe')

TRPA Block Groups

tmap_mode("view")
tm_shape(block_group)+ tm_polygons()